Generic object classification for autonomous robots

Un dels principals problemes de la interacció dels robots autònoms és el coneixement de l'escena. El reconeixement és fonamental per a solucionar aquest problema i permetre als robots interactuar en un escenari no controlat. En aquest document presentem una aplicació pràctica de la captura d'objectes, de la normalització i de la classificació de senyals triangulars i circulars. El sistema s'introdueix en el robot Aibo de Sony per a millorar-ne la interacció. La metodologia presentada s'ha comprobat en simulacions i problemes de categorització reals, com ara la classificació de senyals de trànsit, amb resultats molt prometedors.Uno de los principales problemas de la interacción de los robots autónomos es el conocimiento de la escena. El reconocimiento es fundamental para solventar este problema y permitir a los robots interactuar en un escenario no controlado. En este documento, presentamos una aplicación práctica de captura del objeto, normalización y clasificación de señales triangulares y circulares. El sistema es introducido en el robot Aibo de Sony para mejorar el comportamiento de la interacción del robot. La metodología presentada ha sido testeada en simulaciones y problemas de categorización reales, como es la clasificación de señales de tráfico, con resultados muy prometedores.One of the main problems of autonomous robots interaction is the scene knowledge. Recognition is concerned to deal with this problem and to allow robots to interact in uncontrolled environments. In this paper, we present a practical application for object fitting, normalization and classification of triangular and circular signs. The system is introduced in the Aibo robot of Sony to increase the robot interaction behaviour. The presented methodology has been tested in real simulations and categorization problems, as the traffic signs classification, with very promising results.Nota: Aquest document conté originàriament altre material i/o programari només consultable a la Biblioteca de Ciència i Tecnologia

Video-based Smoke Detection Algorithms: A Chronological Survey

Over the past decade, several vision-based algorithms proposed in literature have resulted into development of a large number of techniques for detection of smoke and fire from video images. Video-based smoke detection approaches are becoming practical alternatives to the conventional fire detection methods due to their numerous advantages such as early fire detection, fast response, non-contact, absence of spatial limits, ability to provide live video that conveys fire progress information, and capability to provide forensic evidence for fire investigations. This paper provides a chronological survey of different video-based smoke detection methods that are available in literatures from 1998 to 2014.Though the paper is not aimed at performing comparative analysis of the surveyed methods, perceived strengths and weakness of the different methods are identified as this will be useful for future research in video-based smoke or fire detection. Keywords: Early fire detection, video-based smoke detection, algorithms, computer vision, image processing

Modelos de aprendizaje automático en la detección e identificación de personas: una revisión de literatura

Introduction: This article is the result of research entitled "Development of a prototype to optimize access conditions to the SENA-Pescadero using artificial intelligence and open-source tools", developed at the Servicio Nacional de Aprendizaje in 2020.   Problem: How to identify Machine Learning Techniques applied to computer vision processes through a literature review? Objective: Determine the application, as well as advantages and disadvantages of machine learning techniques focused on the detection and identification of people. Methodology: Systematic literature review in 4 high-impact bibliographic and scientific databases, using search filters and information selection criteria. Results: Machine Learning techniques defined as Principal Component Analysis, Weak Label Regularized Local Coordinate Coding, Support Vector Machines, Haar Cascade Classifiers and EigenFaces and FisherFaces, as well as their applicability in detection and identification processes.   Conclusion: The research led to the identification of the main computational intelligence techniques based on machine learning, applied to the detection and identification of people. Their influence was shown in several application cases, but most of them were focused on the implementation and optimization of access control systems, or tasks in which the identification of people was required for the execution of processes. Originality: Through this research, we studied and defined the main machine learning techniques currently used for the detection and identification of people. Limitations: The systematic review is limited to information available in the 4 databases consulted, and the amount of information is variable as articles are deposited in the databases.Introducción: Este artículo es el resultado de la investigación titulada " Desarrollo de un prototipo para optimizar las condiciones de acceso al SENA-Pescadero utilizando inteligencia artificial y herramientas de código abierto", desarrollada en el Servicio Nacional de Aprendizaje en 2020. Problema: ¿Cómo identificar las técnicas de aprendizaje automático aplicadas a los procesos de visión por computador a través de una revisión bibliográfica? Objetivo: Determinar la aplicación, así como las ventajas y desventajas de las técnicas de aprendizaje automático enfocadas a la detección e identificación de personas. Metodología: Revisión sistemática de la literatura en 4 bases de datos bibliográficas y científicas de alto impacto, utilizando filtros de búsqueda y criterios de selección de información. Resultados: Técnicas de aprendizaje automático definidas como Análisis de Componentes Principales, Codificación Local de Coordenadas Regularizada de Etiquetas Débiles, Máquinas de Vectores de Soporte, Clasificadores en Cascada de Haar y EigenFaces y FisherFaces, así como su aplicabilidad en procesos de detección e identificación. Conclusiones: La investigación permitió identificar las principales técnicas de inteligencia computacional basadas en machine learning aplicadas a la detección e identificación de personas. Su influencia se mostró en varios casos de aplicación, pero la mayoría de ellos se centraron en la implementación y optimización de sistemas de control de acceso, o tareas en las que se requería la identificación de personas para la ejecución de procesos Originalidad: A través de esta investigación se estudiaron y definieron las principales técnicas de machine learning utilizadas actualmente para la detección e identificación de personas

Интеллектуальный видеоанализ опасных ситуаций

[For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version)].The work was supported by the Russian Foundation for Basic Research (Grant No. 17-20-03034). ABSTRACT The article is devoted to development of a system for the intelligent analysis of video recordings of external surveillance cameras, which makes it possible to identify dangerous situations at railway facilities using the example of detection of falls in the track area. A method of preprocessing a video for the purpose of forming a feature space based on the use of background subtraction using the Gaussian mixture method, followed by tracking the movement of a person with the help of the Kalman filter and deformation of the shape of the mobile object as a result of applying the procrustean analysis is proposed. The selection of the optimal composition of the feature space and additional heuristics providing the isolation of episodes of falls from video recording with an average quality of the Cohen’s kappa 0,62 is compared with the visual analysis by the operator. Keywords: railway, safety, video surveillance, intelligent video analysis, motion recognition, machine learning, form analysis.Текст аннотации на англ. языке и полный текст статьи на англ. языке находится в прилагаемом файле ПДФ (англ. версия следует после русской версии).Работа выполнена при поддержке Российского фонда фундаментальных исследований (грант № 17-20-03034). Статья посвящена разработке системы интеллектуального анализа видеозаписей камер наружного наблюдения, позволяющей выявлять опасные ситуации на объектах железных дорог на примере детекции падений в зоне пути. Предложен метод предобработки видеоряда с целью формирования пространства признаков, основанный на использовании вычитания фона по методу гауссовой смеси, последующем отслеживании перемещения человека при помощи фильтра Калмана и деформации формы подвижного объекта в результате применения прокрустова анализа. Обоснован подбор оптимального состава пространства признаков и дополнительных эвристик, обеспечивающих выделение эпизодов падений по видеозаписи со средним качеством каппы Коэна 0,62 по сравнению с визуальным анализом оператором

A framework for cardio-pulmonary resuscitation (CPR) scene retrieval from medical simulation videos based on object and activity detection.

In this thesis, we propose a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical students, where an emergency patient condition is simulated on human-like mannequins and the students act upon. These simulation sessions are recorded by the physician, for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. The proposed framework for CPR scene retrieval, would eliminate the conventional approach of using shot detection and frame segmentation techniques. Firstly, our work explores the application of Histogram of Oriented Gradients in three dimensions (HOG3D) to retrieve the scenes containing CPR activity. Secondly, we investigate the use of Local Binary Patterns in Three Orthogonal Planes (LBPTOP), which is the three dimensional extension of the popular Local Binary Patterns. This technique is a robust feature that can detect specific activities from scenes containing multiple actors and activities. Thirdly, we propose an improvement to the above mentioned methods by a combination of HOG3D and LBP-TOP. We use decision level fusion techniques to combine the features. We prove experimentally that the proposed techniques and their combination out-perform the existing system for CPR scene retrieval. Finally, we devise a method to detect and retrieve the scenes containing the breathing bag activity, from the medical simulation videos. The proposed framework is tested and validated using eight medical simulation videos and the results are presented

A comprehensive review of vehicle detection using computer vision

A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection